Parallel Magnetic Resonance Imaging (MRI) reconstruction combines the signals of multiple coil elements to reconstruct an image. This technique may be used to improve the signal-to-noise ratio, as well as to accelerate acquisition and reduce scan time. One drawback of Parallel MRI reconstruction is that it can involve large data sets. For example, specific Parallel MRI techniques such as sampling perfection with application-optimized contrasts using different flip-angle evolutions (SPACE) imaging mandate high resolution 3D reconstructions of the order of 306×448×288. Techniques such as compressed-sensing may be used to reduce the size of the data acquired somewhat, but the size of data items such as the coil sensitivity maps still require a great deal of storage availability on the computer performing the reconstruction.
Parallel computing programming platforms offer the potential of reducing reconstruction times. These platforms use parallel hardware such as graphical processing units (GPUs), as well as non-GPU hardware, to execute operations at a speed that is significantly higher than those available on systems without parallel hardware. However, memory is limited on such parallel hardware and, as such, parallel computing programming platforms could not be used for many Parallel MRI applications. Thus, it is desired to create new techniques for Parallel MRI which improve the memory efficiency and allow processing on parallel computing platforms.